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A cross-species foundation model for single cells

细胞研究（英文版） 2024年第12期34卷 818-819页

作者： Korbinian Traeuble Matthias Heinig Institute of Computational Biology German Research Center for Environmental HealthHelmholtz Zentrum MünchenNeuherbergGermany Department of Computer Science TUM School of ComputationInformation and TechnologyTechnical University of MunichGarchingGermany Institute of Computational Biology German Research Center for Environmental HealthHelmholtz Zentrum MünchenNeuherbergGermany Department of Computer Science TUM School of ComputationInformation and TechnologyTechnical University of MunichGarchingGermany German Center for Cardiovascular Research partner site Munich Heart AllianceBerlinGermany

Foundation models in transcriptomics have gained attention due to their ability to generalize across tasks with limited labeled *** builds upon these models by incorporating prior biological knowledge and datasets from both human and mouse cells,enhancing its capacity for cross-species analysis and advancing the field of single-cell transcriptomics.

关键词： cells single model cross-species foundation

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Deep Learning-based Computational Job Market Analysis: A Survey on Skill Extraction and Classification from Job Postings 1

Deep Learning-based Computational Job Market Analysis: A Sur...

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1st Workshop on Natural Language Processing for Human Resources, NLP4HR 2024

作者： Senger, Elena Zhang, Mike van der Goot, Rob Plank, Barbara MaiNLP Center for Information and Language Processing LMU Munich Germany Department of Computer Science IT University of Copenhagen Denmark Fraunhofer Center for International Management and Knowledge Economy IMW Germany

ISBN: (纸本)9798891760769

Recent years have brought significant advances to Natural Language Processing (NLP), which enabled fast progress in the field of computational job market analysis. Core tasks in this application domain are skill extraction and classification from job postings. Because of its quick growth and its interdisciplinary nature, there is no exhaustive assessment of this emerging field. This survey aims to fill this gap by providing a comprehensive overview of deep learning methodologies, datasets, and terminologies specific to NLP-driven skill extraction and classification. Our comprehensive cataloging of publicly available datasets addresses the lack of consolidated information on dataset creation and characteristics. Finally, the focus on terminology addresses the current lack of consistent definitions for important concepts, such as hard and soft skills, and terms relating to skill extraction and classification. © 2024 Association for Computational Linguistics.

关键词： Terminology

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quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions

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Physical Review B 2025年第2期111卷 024409-024409页

作者： Patrick Adelhardt Antonia Duft Kai Phillip Schmidt Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Department Physik Staudtstraße 7 D-91058 Erlangen Germany Joint Quantum Institute and Joint Center for Quantum Information and Computer Science University of Maryland Department of Physics and National Institute of Standards and Technology College Park Maryland 20742 USA

We study the XXZ square lattice bilayer model with antiferromagnetic nonfrustrating long-range interactions that decay as a power law with the distance. Employing large-scale high-order series expansions with classical Monte Carlo integration (pCUT+MC) about the limit of isolated Heisenberg dimers in the rung-singlet phase, we investigate the one-triplon dispersion and the corresponding spectral weight along the parameter axes of the long-range decay exponent and the XXZ anisotropy. By tuning the latter, we observe two extended regions of 3D XY and Ising universality as well as 3D Heisenberg critical exponents at the isotropic point. Along the decay exponent axis, we demonstrate mean-field behavior for strong long-range couplings, the aforementioned three universality classes for sufficiently weak interactions, and continuously varying critical exponents in between. Using extrapolations, we are able to determine the one-triplon dispersion in a quantitative fashion up to the quantum-critical breakdown of the rung-singlet phase. This allows us to extract the dynamical critical exponent z as a function of the decay exponent, displaying a universal behavior. The detected z<1 for small decay exponents is in agreement with the expected properties of the anomalous Goldstone modes in the ordered phases with broken continuous symmetry.

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TAEM 2.0: A Faster Transfer-Aware Effective Loop Mapping for Heterogeneous Resources on CGRA

TAEM 2.0: A Faster Transfer-Aware Effective Loop Mapping for...

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作者： Kou, Mingyang Gu, Jiangyuan Yao, Hailong Wei, Shaojun Yin, Shouyi Tsinghua University Department of Computer Science and Technology Beijing100084 China Institute of Microelectronics Tsinghua University Beijing100084 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Beijing100084 China

Coarse-grained reconfigurable architectures (CGRAs) are energy-efficient and processing-flexible platforms to perform parallel computation. CGRAs combine the advantages of flexibility of general-purpose processors (GPPs) and energy efficiency of application-specific integrated circuits (ASICs). During the compilation process, the CGRA compiler needs to convert the high-level language codes into a data flow graph, and then map it onto CGRA to generate instruction flow and configuration context. The instruction mapping schemes of the CGRA compiler have a great impact on the efficiency and energy consumption of CGRAs. Furthermore, the quality of the instruction mapping schemes of the CGRA compiler highly depends on how the compiler maps data dependencies using different CGRA resources. This article proposes an enhanced transfer-aware loop mapping method, TAEM 2.0, based on state-of-the-art TAEM algorithm. Based on a parallel iterative IBBMCX algorithm and comprehensive CGRA resources analysis strategy, this method efficiently processes the complex situations of utilizing all those heterogeneous resources on CGRA and significantly accelerates the compilation process. Experimental results show TAEM 2.0 can accelerate the compilation process by 4.40 × while generating the same or better mapping results on CGRA, when compared to the state-of-art mapping technique. © 1982-2012 IEEE.

关键词： Data transfer

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Sound field interpolation with unsupervised calibration for freely spaced circular microphone array in rotation-robust beamforming 31

Sound field interpolation with unsupervised calibration for ...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Luan, Shuming Wakabayashi, Yukoh Toda, Tomoki Graduate School of Information Science Nagoya University Nagoya Japan Department of Computer Science Toyohashi University of Technology Toyohashi Japan Information Technology Center Nagoya University Nagoya Japan

ISBN: (纸本)9789464593600

This paper introduces a new approach to perform unequally spaced sound field interpolation (SFI) for beamforming using a freely spaced circular microphone array (CMA) that is robust to rotation. Unlike previous methods that required a known microphone distribution, the proposed approach uses unsupervised calibration to estimate the error angle of each microphone. This is accomplished through an innovative optimization problem, making it practical for CMAs with freely spaced microphones. By using only the number of channels and rotation angle, the proposed method enables accurate unequally spaced SFI. Experimental results show that the proposed method outperforms previous methods on freely spaced CMAs. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Calibration

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Compression of quantum Shallow-Circuit States

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Physical Review Letters 2025年第1期134卷 010603-010603页

作者： Yuxiang Yang QICI Quantum Information and Computation Initiative Department of Computer Science The University of Hong Kong Pokfulam Road Hong Kong SAR China

Shallow quantum circuits feature not only computational advantages over their classical counterparts but also cutting-edge applications. Storing quantum information generated by shallow circuits is a fundamental question of both theoretical and practical importance that remained largely unexplored. In this Letter, we show that N copies of an unknown n-qubit state generated by a fixed-depth circuit can be compressed into a hybrid memory of O(nlog2N) (qu)bits, which achieves the optimal scaling of memory cost. Our work shows that the computational complexity of resources can significantly impact the rate of quantum information processing, offering a unique and unified view of quantum Shannon theory and quantum computing.

关键词： quantum electronics

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Medical Diagnoses: Heart Disease Prediction 2

Medical Diagnoses: Heart Disease Prediction

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Mahfuri, Mahmoud Ghazal, Taher M. Mudassar, Muhammad Siddiqui, Shahan Yamin Farooq, Sajid Kanwal, Nayab Ahmad, Munir International University for Science and Technolgy Computer and Information Technolgy Department Faculty of Engineering Syria Center for Cyber Security Faculty of Information Science and Technology Selangor Bangi43600 Malaysia Skyline University College School of Information Technology University City Sharjah 1797 United Arab Emirates The University of Lahore Lahore Pakistan Minhaj University Department of Computer Science Lahore Pakistan School of Computer Science National College of Business Administration & Economics Lahore Pakistan NCBA&E School of Computer Science Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The successful and continuously increasing applications of machine learning (ML) and data mining (DM) in the wide range of fields such as e-businesses, public or private sectors, marketing and retails has led the ML and DM applications in other industries and health sectors. In the health sector the data is information wise too rich but not knowledge rich. It can be due to the lack of effective tools and research study in this era. In this research work, an efficient model is proposed based on the machine learning and data mining tools to predict a particular medical disease that is the heart disease. The solution for the problem which is addressed that data is not knowledge-rich, is presented in this paper. Waikato Environment and Python is used for to analyze the data, so that an automation system can be formed. Several experiments are to be implemented to compare the performance through Supervised Learning and Unsupervised Learning. Bayesian learning (Naïve Bayes), Tree's algorithm (Decision Tree), and Neural Networks (MLPNN) are applied on the same dataset. Comparatively, Decision Tree gives best accuracy when compared with Naïve Bayes, ANN performs more effective when compared with others two Bayesian algorithms (Naïve Bayes) and Tree's algorithm (Decision tree). © 2023 IEEE.

关键词： Decision trees

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Empowering Smart Homes with Fog Computing for IoT Connectivity

Empowering Smart Homes with Fog Computing for IoT Connectivi...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Mahdi, Ali Jafer Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Abdallah Hashim, Aisha Hassan Center for Cyber Security Faculty of Information Science and Technology Universiti Kebangsaan Malaysia Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq AI-Zahraa University for Women Department of Scientific Affairs Bangi43600 Malaysia College of Computer and Information Technology University of Anbar Computer Networking Systems Department Anbar Iraq Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia International Islamic University Malaysia Faculty of Engineering Department of Electrical and Computer Engineering Selangor Malaysia

ISBN: (纸本)9798350369106

Innovative grid technology leverages information and Communication Technology (ICT) to enhance energy efficiency and mitigate losses. This paper introduces a 'novel three-tier hierarchical framework for smart homes within the Internet of Things (IoT)' ecosystem, addressing the current shortage of intelligent solutions that underutilize IoT advantages. Our framework's primary objective is to elevate smart homes to the microgrid level, enabling the seamless integration from the microgrid's renewable distributed energy sources and attaining maximum energy efficiency. In addition to traditional data processing, we propose the application of fog computing in smart homes. Through a rigorous simulation using real smart meter datasets, our study demonstrates that fog computing, mainly when using predictive filtering, may drastically cut down on the amount of data being sent and the load on the home's network. This innovation holds immense potential for revolutionizing energy management and resource allocation in the context of smart homes, thus contributing to a sustainable and efficient future. This research represents a critical step towards harnessing the power of IoT and fog computing in smart grids and homes, offering a promising path towards more intelligent, eco-friendly, and energy-efficient living spaces. © 2024 IEEE.

关键词： Smart homes

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Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe HOU Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

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Advancements in Liver Tumor Detection:A Comprehensive Review of Various Deep Learning Models

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computer Modeling in Engineering & sciences 2025年第1期142卷 91-122页

作者： Shanmugasundaram Hariharan D.Anandan Murugaperumal Krishnamoorthy Vinay Kukreja Nitin Goyal Shih-Yu Chen Deptartment of Artificial Intelligence and Data Science Vardhaman College of EngineeringHyderabad501218India Deptartment of CSE V.S.B College of EngineeringKarur639111India Deptartment of Electrical and Electronics Engineering Vardhaman College of EngineeringHyderabad501218India Centre for Research Impact&Outcome Chitkara UniversityRajpura140401PunjabIndia Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendergarh123031HaryanaIndia Department of Computer Science and Information Engineering National Yunlin University of Science and TechnologyYunlin64002Taiwan Intelligence Recognition Industry Service Research Center National Yunlin University of Science and TechnologyYunlin64002Taiwan

Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment *** Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and *** review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and *** metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical *** review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging *** suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular *** paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.

关键词： Liver tumor detection liver tumor segmentation image processing liver tumor diagnosis feature extraction tumor classification deep learning machine learning

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